All Implemented Interfaces:
Function<Vector,Matrix>, RntoMatrix

public class VARMAAutoCovariance extends MultivariateAutoCovarianceFunction
Compute the Auto-CoVariance Function (ACVF) for a vector AutoRegressive Moving Average (ARMA) model, assuming that EXt = 0.

This implementation solves the Yule-Walker equation.

The R equivalent functions are ARMAacf and TacvfAR in package FitAR.

  • Constructor Details

    • VARMAAutoCovariance

      public VARMAAutoCovariance(VARMAModel model, int nLags)
      Compute the auto-covariance function for a vector ARMA model.

      To solve Eq. 11.3.15, we "expand" the (p+1) matrix equations into (p+1)*m*m linear equations. m is the dimension of Γ (ACVF).

      model - an ARIMA model
      nLags - the number of lags
  • Method Details

    • evaluate

      public Matrix evaluate(double i, double j)
      Description copied from class: R2toMatrix
      Evaluate f(x1, x2) = A.
      Specified by:
      evaluate in class R2toMatrix
      i - x1
      j - x2
      f(x1, x2)
    • evaluate

      public Matrix evaluate(double i)
      Get the i-th auto-covariance matrix.
      i - the lag order
      the i-th auto-covariance matrix